

import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      __a    :List[Any]      =			np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      __a    :Union[str, Any]      =			[0, 25, 50]
      __a    :Any      =			[25, 50, 75]
      __a    :Tuple      =			fuzz.membership.trimf(X, abca)
      __a    :List[Any]      =			fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      __a    :Union[str, Any]      =			np.ones(75)
      __a    :Tuple      =			np.zeros((75,))
      # 1. Union = max(µA(x), µB(x))
      __a    :Optional[Any]      =			fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      __a    :Optional[int]      =			fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      __a    :List[str]      =			fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      __a    :List[Any]      =			fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      __a    :Union[str, Any]      =			young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      __a    :Any      =			young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      __a    :Any      =			fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      __a    :Optional[int]      =			fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title('Young')
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title('Middle aged')
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title('union')
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title('intersection')
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title('complement_a')
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title('difference a/b')
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title('alg_sum')
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title('alg_product')
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title('bdd_sum')
      plt.grid(True)

      plt.subplot(4, 3, 10)
      plt.plot(X, bdd_difference)
      plt.title('bdd_difference')
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






